Vehicles operating in an autonomous mode (e.g., driverless) can relieve occupants, especially the driver, from some driving-related responsibilities. When operating in an autonomous mode, the vehicle can navigate to various locations using onboard sensors, allowing the vehicle to travel with minimal human interaction or in some cases without any passengers.
A perception of a surrounding environment may represent what an ordinary driver would perceive surrounding a vehicle in which the driver is driving. Different sensory systems for an autonomous driving vehicle may have a limited perception. For example, perceptions based on camera images are missing depth information. Perceptions based on LIDAR and RADAR images may be limited to black and white. Moreover, accuracy of LIDAR and RADAR can be dependent on weather conditions and/or distances to perceived objects.
Range measurement can be calculated for any ground plane pixels (assuming a relatively flat ground plane) captured by camera images once calibration parameters (such as a dynamic change in pitch angle between the camera and the ground plane) of the camera capturing the images are known. However range measurements based on images captured by camera devices can be inaccurate due to a change in the pitch angle (e.g., the vehicle being tilted) of the ADV and/or camera devices while the ADV is in operation. Thus, there is a need to estimate a change in pitch angle of the ADV to improve an accuracy of range measurements for ground pixels using monocular cameras.